The study examines how differences in radiologists’ reporting styles—such as terminology, shorthand, formatting, and detail—affect the evaluation of AI-generated chest X‑ray reports. By quantifying the sensitivity of common metrics to these variations, the authors show that changes in reference reports can shift model rankings. They introduce a taxonomy of reporting variations and a rewriting method, ReRef, that preserves clinical meaning while altering style, and release a validated dataset of paired reference reports to aid future research.
arXiv:2607. 26333v1 Announce Type: cross Abstract: Chest X-ray (CXR) machine learning relies heavily on automated evaluation using reference standards that aim to approximate clinical judgment.
Sampling multiple reports creates an inference problem: how should sampled evidence be converted into a final decision, and does a better structured decision yield a better report? We study these ques...
The paper evaluates large language model (LLM) judges for radiology report assessment across multiple imaging modalities and anatomical regions, comparing four existing metrics (RadFact, GREEN, FineRadScore, CRIMSON) with a new metric called VERT. GREEN emerges as the most robust among current metrics, while VERT improves correlation with radiologist judgments by up to 26% over GREEN. The study also shows that ensembling and lightweight fine‑tuning of the Qwen3‑30B model can yield additional performance gains and significant latency reductions.
arXiv:2411. 15122v2 Announce Type: replace-cross Abstract: AI-driven models have demonstrated significant potential in automating radiology report generation for chest X-rays.
arXiv:2609.01470v1 Announce Type: new Abstract: As AI systems are increasingly used to draft radiology reports, reliably evaluating their clinical quality remains a critical challenge. Large language...
arXiv:2609.27607v1 Announce Type: cross Abstract: An AI-generated radiology report can resemble a physician's report while omitting an abnormality, adding an unsupported finding, or reversing its pre...
arXiv:2607. 25589v1 Announce Type: cross Abstract: Medical-imaging AI benchmarks combine datasets, DICOM rendering, prompts, provider APIs, automated labels, statistical code, manuscripts, and repository releases.
arXiv:2605.30984v2 Announce Type: replace-cross Abstract: Modern 3D medical vision-language models (VLMs) can generate fluent radiology-style text while exhibit critically low pathology detection and...
arXiv:2604.09537v2 Announce Type: replace Abstract: A grounding verifier checks a statement about a patient's image against the radiology report and the cited evidence. The state-of-the-art methods f...
arXiv:2509. 19671v3 Announce Type: replace Abstract: Public datasets of Chest X-Rays (CXRs) have long been a popular benchmark for developing machine learning (ML) computer vision models in healthcare.
arXiv:2607. 05880v1 Announce Type: cross Abstract: Imaging demand is growing faster than the radiology workforce can expand, and reporting backlogs cannot be resolved through training and recruitment alone.
arXiv:2601.16753v2 Announce Type: replace-cross Abstract: Longitudinal information in radiology reports refers to the sequential tracking of findings across multiple examinations over time, which is...